A study on the recognition and classification of embroidered textile defects in manufacturing. Issue 4 (March 2016)

• Record Type: Journal Article
• Authors: Kuo, Chung-Feng Jeffrey; Juang, Yun
• Is Part Of: Textile research journal. Volume 86:Issue 4(2016)
• Page Start: 393
• Page End: 408
• Publication Date: 2016-03
• Subjects: embroidery defect -- characteristic procedure classification -- back-propagation neural network -- embroidered textile pattern recognition; Textile industry -- Periodicals; Textile fabrics -- Periodicals; Textile research -- Periodicals; Textiles et tissus -- Industrie et commerce -- Périodiques; Textiles et tissus -- Périodiques; Textiles et tissus -- Recherche -- Périodiques; Electronic journals; 677.005
• DOI: 10.1177/0040517515590410
• Languages: English
• ISSNs: 0040-5175

Abstract

Embroidered textile is a highly valued artwork. Three-dimensional patterns can be created by variable stitches and the material characteristics of embroidery thread. At present, computer plate-making is widely used in embroidery operation, allowing factories to mass-produce efficiently, but in the quality control stage, manpower is needed for visual inspections. The embroidered textile is classified after quality control. The conforming products are classified into three different categories: non-defective products, defective products but improvable after reworking and re-inspection, and disposable products when defective products cannot be improved. However, there is no specific standard for embroidered textile defects. In order to increase efficiency of human resources, elucidate the defects on embroidery, and achieve automated defect classification, we proposed methods on the recognition and classification of embroidered textile defects. After interviewing producers of embroidered textiles and manufacturers of computer embroidery machines, we identified four types of defect in embroidery textile patterns: foundation yarn floating knit, stitch missing, joint defect, and misregistration for defect recognition. The back-propagation neural network (BPNN) and characteristic procedure classification were used for defect classification. The characteristic procedure classification yielded more accurate results (accuracy rate: training: 100%, testing: 100%), but the process requires more time and cost for training. The BPNN requires the least amount of time, but the recognition rate is slightly lower (accuracy rate: training: 100%, testing: 98.80%) when compared to the characteristic procedure classification. The embroidered textile pattern recognition and classification methods proposed by this study are expected to provide an automated inspection procedure for the embroidery textile industry.